Negative mood states that characterize drug withdrawal are partly under genetic control and have been associated with craving and relapse to drug use in humans. Despite the fact that a large majority of amphetamine (AMP) users report experiencing negative mood states (such as dysphoria, anhedonia, and anxiety) during withdrawal, AMP withdrawal has been much less studied than nicotine, ethanol, and morphine withdrawal. Mice can be used to model aspects of the negative mood states associated with AMP withdrawal and offer a number of advantages relative to studies in humans. We are proposing to fine map quantitative trait loci (QTLs) affecting negative mood states associated with AMP withdrawal using commercially available, outbred CFW mice. Outbred mice are powerful populations for fine mapping of QTLs because each individual possesses a large number of recombinations over the course of many generations of outbreeding. These recombinations break down linkage disequilibrium (LD) between the QTL and surrounding markers, allowing high resolution mapping of the variants that influence quantitative traits. High resolution mapping is critically important to the future of QTL studies. Presently less than 1% of all QTLs identified in mice have led to successful gene identification. This is because traditional mapping populations are good for identifying broad regions that contain QTLs (coarse mapping), but have too much LD to permit fine mapping. The goal of this proposal is to implement a genetic strategy that is similar to human genome-wide association studies (GWAS) and will identify specific genes that are associated with variation in AMP withdrawal severity. We will phenotype 1300 CFW mice for a battery of behavioral and physiological traits associated with the negative mood states that characterize drug withdrawal. Mice will be densely genotyped at ~150,000 markers using a genotyping-by-sequencing (GBS) approach. We will then perform GWAS to identify QTLs for all phenotypes measured. We will also employ next-generation sequencing of mRNA (RNA-Seq) obtained from key brain regions to identify gene expression differences in a subset of these mice. These data will be used to map expression QTLs (eQTLs) and to identify coding polymorphisms. By identifying single nucleotide polymorphisms (SNPs) that are associated with both behavioral and gene-expression traits we can rapidly identify plausible biological explanations for how these SNPs influence behavior. Through genetic manipulation, such hypotheses are directly testable in mice, which is a major advantage of performing GWAS in mice versus humans. The methods proposed in this application are generally applicable to any quantitative trait and have the potential to vastly accelerate the process of gene identification. If we understand the pathways linking genetic variation and expression to neuronal function and behavior in mice, then it may be possible to target specific molecules to prevent and treat drug use disorders in humans.